Methods and systems for identifying compatible meal options

ABSTRACT

A system for identifying compatible meal options. The system includes a body analysis module configured to receive a user biological marker, select a clustering dataset from a clustering database, generate a hierarchical clustering algorithm and assign a plurality of user body measurements to a first classified dataset cluster. The system includes a food analysis module configured to select a food training set from a food database, generate using a supervised machine-learning process a food model, generate a food tolerance instruction set, and display on a graphical user interface the food tolerance instruction set. The system includes a menu generator module configured to select a menu training set from a menu database, generate using a supervised machine-learning process a menu model that produces an output containing a plurality of menu options, and display on a graphical user interface the plurality of menu options. The system includes a local selector module configured to receive a plurality of meal option inputs from a meal preparer device, generate a k-nearest neighbors algorithm, identify a plurality of compatible meal options, and display the plurality of compatible meal options on a graphical user interface.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for identifying compatible meal options.

BACKGROUND

Accurate identification of compatible meal options can be challenging.Analyzing multiple user demands and requirements can be complex.Further, this can be complicated by large quantities of data to beanalyzed to locate and identify compatible meal options.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for identifying compatible meal options. Thesystem includes a processor wherein the processor further comprises abody analysis module wherein the body analysis module is furtherconfigured to receive a user biological marker wherein the userbiological marker contains a plurality of user body measurements; selecta clustering dataset from a clustering database wherein the clusteringdataset further comprises a plurality of unclassified datapoints;generate a hierarchical clustering algorithm using the clusteringdataset as input and wherein the hierarchical clustering algorithmoutputs a definite number of classified dataset clusters each containinga cluster label; assign the plurality of user body measurements to afirst classified dataset cluster containing a cluster label; and selectthe first classified dataset cluster containing the cluster label. Thesystem includes a food analysis module wherein the food analysis moduleis further configured to receive from the body analysis module the firstclassified dataset cluster containing the cluster label and the userbiological marker; select a food training set from a food database as afunction of the user biological marker wherein the food training setcorrelates user body measurements to food tolerance scores; generateusing a supervised machine-learning process a food model that receivesthe first assigned user body data element as an input and produces anoutput containing a food tolerance score; generate a food toleranceinstruction set using the food tolerance score; and display on agraphical user interface located on the processor the output containingthe food tolerance instruction set containing the food tolerance score.The system includes a menu generator module wherein the menu generatormodule is further configured to receive the food tolerance instructionset from the food analysis module; select a menu training set from amenu database as a function of the food tolerance instruction setwherein the menu training set correlates food tolerance scores to menuoptions; generate using a supervised machine-learning process a menumodel that receives the food tolerance instruction set as an input andproduces an output containing a plurality of menu options utilizing themenu training set; and display on the graphical user interface locatedon the processor the output containing the plurality of menu options.The system includes a local selector module wherein the local selectormodule is further configured to receive a plurality of meal optioninputs from a meal preparer device wherein the meal option inputscontain available menu listings; receive the output containing theplurality of menu options from the menu generator module; generate ak-nearest neighbors algorithm utilizing the plurality of meal optioninputs and the plurality of menu options; identify a plurality ofcompatible meal options as a function of generating the k-nearestneighbor algorithm; and display the plurality of compatible meal optionson the graphical user interface located on the processor.

In an aspect, a method of identifying compatible meal options. themethod includes receiving by a processor a user biological markerwherein the user biological marker contains a plurality of user bodymeasurements. The method includes selecting by the processor aclustering dataset from a clustering database wherein the clusteringdataset further comprises a plurality of unclassified datapoints. Themethod includes generating by the processor a hierarchical clusteringalgorithm using the clustering dataset as input and wherein thehierarchical clustering algorithm outputs a definite number ofclassified dataset clusters each containing a cluster label. The methodincludes assigning by the processor the plurality of user bodymeasurements to a first classified dataset cluster containing a clusterlabel. The method includes selecting by the processor the firstclassified dataset cluster containing the cluster label. The methodincludes selecting by the processor a food training set from a fooddatabase as a function of the user biological marker wherein the foodtraining set correlates user body measurements to food tolerance scores.The method includes generating by the processor using a supervisedmachine-learning process a food model that receives the first assigneduser body data element as an input and produces an output containing afood tolerance score. The method includes generating by the processor afood tolerance instruction set using the food tolerance score. Themethod includes displaying by the processor on a graphical userinterface the output containing the food tolerance instruction setcontaining the food tolerance score. The method includes selecting bythe processor a menu training set from a menu database as a function ofthe food tolerance instruction set wherein the menu training setcorrelates food tolerance scores to menu options. The method includesgenerating by the processor using a supervised machine-learning processa menu model that receives the food tolerance instruction set as aninput and produces an output containing a plurality of menu optionsutilizing the menu training set. The method includes displaying by theprocessor on the graphical user interface located the output containingthe plurality of menu options. The method includes receiving by theprocessor a plurality of meal option inputs from a meal preparer devicewherein the meal option inputs contain available menu listings. Themethod includes generating by the processor a k-nearest neighborsalgorithm utilizing the plurality of meal option inputs and theplurality of menu options. The method includes identifying by theprocessor a plurality of compatible meal options as a function ofgenerating the k-nearest neighbor algorithm. The method includesdisplaying by the processor the plurality of compatible meal options onthe graphical user interface.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for identifying compatible meal options;

FIG. 2 is a block diagram illustrating an exemplary embodiment of abiological marker database;

FIG. 3 is a block diagram illustrating an exemplary embodiment of a bodyanalysis module;

FIG. 4 is a block diagram illustrating an exemplary embodiment of aclustering database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of a foodanalysis module;

FIG. 7 is a block diagram illustrating an exemplary embodiment of a fooddatabase;

FIG. 8 is a block diagram illustrating an exemplary embodiment of a menugenerator module;

FIG. 9 is a block diagram illustrating an exemplary embodiment of a menudatabase;

FIG. 10 is a block diagram illustrating an exemplary embodiment of alocal selector module;

FIG. 11 is a process flow diagram illustrating an exemplary embodimentof a method of identifying compatible meal options; and

FIG. 12 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tomethods and systems for identifying compatible meal options. In anembodiment, a processor receives a user biological marker containing aplurality of user body measurements. A processor selects a clusteringdataset from a clustering database and generates a hierarchicalclustering algorithm to output a definite number of classified datasetclusters each containing a cluster label. A processor assigns a userbiological marker containing a plurality of user body measurements to afirst classified dataset clustering containing a cluster label. Aprocessor selects a food training set from a food database as a functionof a user biological marker. A food training set correlates user bodymeasurements to food tolerance scores. A processor generates using asupervised machine-learning process a food model that receives the firstassigned user body data element as input and produces an outputcontaining a food tolerance score. A processor generates a foodtolerance instruction set using a food tolerance score. A processordisplays on a graphical user interface an output containing a foodtolerance instruction set. A processor selects a menu training set froma menu database. A menu training set correlates food tolerance scores tomenu options. A processor generates using a supervised machine-learningprocess a menu model that receives a food tolerance instruction set asan input and produces an output containing a plurality of menu optionsutilizing the menu training set. A processor displays on a graphicaluser interface an output containing a plurality of menu options. Aprocessor receives a plurality of meal option inputs from a mealpreparer device. A processor generates a k-nearest neighbors algorithmutilizing a plurality of meal option inputs and a plurality of menuoptions. A processor identifies a plurality of compatible meal optionsas a function of generating a k-nearest neighbor algorithm. A processordisplays a plurality of compatible meal options on a graphical userinterface.

Referring now to FIG. 1, an exemplary embodiment of a system 100 foridentifying compatible meal options is illustrated. System 100 includesa processor 104. A processor 104 may include any computing device asdescribed herein, including without limitation a microcontroller,microprocessor 104, digital signal processor 104 (DSP) and/or system ona chip (SoC) as described herein. A processor 104 may be housed with,may be incorporated in, or may incorporate one or more sensors of atleast a sensor. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. A processor 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. A processor 104 with one or moreadditional devices as described below in further detail via a networkinterface device. Network interface device may be utilized forconnecting a processor 104 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. A processor 104 may include but is not limited to, for example,A processor 104 or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. A processor 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. A processor 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. A processor 104 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

With continued reference to FIG. 1, a processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, a processor 104may be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. A processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor 104 cores, or the like; division of tasks between parallelthreads and/or processes may be performed according to any protocolsuitable for division of tasks between iterations. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which steps, sequences of steps, processing tasks,and/or data may be subdivided, shared, or otherwise dealt with usingiteration, recursion, and/or parallel processing.

With continued reference to FIG. 1, system 100 includes a body analysismodule 108, which may be implemented as any hardware and/or softwaremodule. Body analysis module 108 is designed and configured receive auser biological marker 112 wherein the user biological marker 112contains a plurality of user body measurements; select a clusteringdataset from a clustering database 120 wherein the clustering datasetfurther comprises a plurality of unclassified datapoints; generate ahierarchical clustering algorithm 124 using the clustering dataset asinput and wherein the hierarchical clustering algorithm 124 outputs adefinite number of classified dataset clusters each containing a clusterlabel; assign the plurality of user body data elements to a firstclassified dataset cluster 128 containing a cluster label; and select afirst assigned user body data element containing the cluster label.

With continued reference to FIG. 1, body analysis module 108 isconfigured to receive a user biological marker 112 from a biologicalmarker database 112. A “user biological marker 112” as used in thisdisclosure, includes any element of physiological state data.Physiological data may include any data indicative of a person'sphysiological state; physiological state may be evaluated with regard toone or more measures of health of a person's body, one or more systemswithin a person's body such as a circulatory system, a digestive system,a nervous system, or the like, one or more organs within a person'sbody, and/or any other subdivision of a person's body useful fordiagnostic or prognostic purposes. For instance, and without limitation,a particular set of biomarkers, test results, and/or biochemicalinformation may be recognized in a given medical field as useful foridentifying various disease conditions or prognoses within a relevantfield. As a non-limiting example, and without limitation, physiologicaldata describing red blood cells, such as red blood cell count,hemoglobin levels, hematocrit, mean corpuscular volume, mean corpuscularhemoglobin, and/or mean corpuscular hemoglobin concentration may berecognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1, physiological data may be obtainedfrom a physical sample. A “physical sample” as used in this example, mayinclude any sample obtained from a human body of a user. A physicalsample may be obtained from a bodily fluid and/or tissue analysis suchas a blood sample, tissue, sample, buccal swab, mucous sample, stoolsample, hair sample, fingernail sample and the like. A physical samplemay be obtained from a device in contact with a human body of a usersuch as a microchip embedded in a user's skin, a sensor in contact witha user's skin, a sensor located on a user's tooth, and the like.

Continuing to refer to FIG. 1, user biological marker 112 contains aplurality of user body measurements. A “user body measurement” as usedin this disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1, user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1, epigenetic, as used herein, includesany user body measurements describing changes to a genome that do notinvolve corresponding changes in nucleotide sequence. Epigenetic bodymeasurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1, gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1, gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1, gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,campylobacter species, Clostridium difficile, Cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1, microbiome, as used herein, includesecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests, methanebased breath tests, hydrogen based breath tests, fructose based breathtests. Helicobacter pylori breath test, fructose intolerance breathtest, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1, nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1, nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1, genetic as used herein, includes anyinherited trait. Inherited traits may include genetic material containedwith DNA including for example, nucleotides. Nucleotides include adenine(A), cytosine (C), guanine (G), and thymine (T). Genetic information maybe contained within the specific sequence of an individual's nucleotidesand sequence throughout a gene or DNA chain. Genetics may include how aparticular genetic sequence may contribute to a tendency to develop acertain disease such as cancer or Alzheimer's disease.

With continued reference to FIG. 1, genetic body measurement may includeone or more results from one or more blood tests, hair tests, skintests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1, genetic body measurement may includean analysis of COMT gene that is responsible for producing enzymes thatmetabolize neurotransmitters. Genetic body measurement may include ananalysis of DRD2 gene that produces dopamine receptors in the brain.Genetic body measurement may include an analysis of ADRA2B gene thatproduces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vaso-dilation and vaso-constriction of blood vessels.

With continued reference to FIG. 1, genetic body measurement may includeACE gene that is involved in producing enzymes that regulate bloodpressure. Genetic body measurement may include SLCO1B1 gene that directspharmaceutical compounds such as statins into cells. Genetic bodymeasurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fulness after eating. Genetic body measurementmay include MP4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1, genetic body measurement may includeCYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1, metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1.

With continued reference to FIG. 1, body analysis module 108 isconfigured to select a clustering dataset from a clustering database120. A “clustering dataset” as used in this disclosure, is a datasetincluding a plurality of unclassified datapoints. A “datapoint” as usedin this disclosure, includes a discrete unit of information. A datapointmay be derived from a measurement, research, and/or expert input. Adatapoint may include one or more measurements on a single member ofunit of observation. Datapoints may be stored in any suitable dataand/or data type. For instance and without limitation, datapoints mayinclude textual data, such as numerical, character, and/or string data.Textual data may include a standardized name and/or code for a disease,disorder, or the like, codes may include diagnostic codes and/ordiagnosis codes, which may include without limitation codes used indiagnosis classification systems such as The International StatisticalClassification of Diseases and Related Health Problems (ICD). Ingeneral, there is no limitation on forms textual data or non-textualdata used as dataset may take; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousforms which may be suitable for use as dataset consistently with thisdisclosure.

With continued reference to FIG. 1, clustering database 120 may includeany data structure for ordered storage and retrieval of data, which maybe implemented as a hardware or software module. Clustering database 120may be implemented, without limitation, as a relational database, akey-value retrieval datastore such as a NO SQL database, or any otherformat or structure for use as a datastore that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Clustering database 120 may include one or more data entriescontaining one or more clustering datasets. Processor 104 and/or bodyanalysis module 108 may select a clustering dataset from clusteringdatabase 120 by classifying a user body data element to a bodydimension, generating a body dimension label, and selecting a clusteringdataset by matching the body dimension label to a clustering datasetcontaining unclassified datapoints related to the body dimension label.A “body dimension” as used in this disclosure, includes one or morefunctional body systems that are impaired by disease in a human bodyand/or animal body. Functional body systems may include one or more bodysystems recognized as attributing to root causes of disease byfunctional medicine practitioners and experts. Body dimensions mayinclude one or more of the body systems as described above including butnot limited to microbiome, gut-wall, genetics, epigenetics, metabolic,nutrients, environment, toxicity, and the like. A “body dimension label”as used in this disclosure, includes a classification label identifyinga particular body dimension. Classification label may be generatedutilizing any methodology as described herein. Classifying a user bodydata element to a body dimension may include predicting the given bodydimension that a user body data element may relate to. Classifying mayinclude predictive modeling that may map an input variable such as auser body data element to a discrete output variable that includes abody dimension. Classification may be performed using lazy learners,eager learners, and/or classification algorithms that include decisiontrees, Naïve bayes, artificial neural networks, K-nearest neighbor (KNN)and the like as described in more detail below. For instance and withoutlimitation, a user body data element such as positive presence ofGiardia lamblia found in a user stool sample, may be classified to abody dimension such as gut-wall. Clustering datasets may be organizedwithin clustering database 120 by body dimension and indicative as towhat unclassified datapoints may relate to. For instance and withoutlimitation, a clustering dataset that contains unclassified datapointsrelating to particular genetic single nucleotide polymorphisms (SNPS)may be organized within clustering database 120 and contained within adata table relating to genetic body dimension. In an embodiment, aparticular clustering dataset may be organized within clusteringdatabase 120 as relating to one or more body dimensions. Processor 104and/or body analysis module 108 may match a generated body dimensionlabel to a clustering dataset organized with clustering database 120 bycomparing a body dimension label to a particular data table to see ifthey both match and are the same.

With continued reference to FIG. 1, body analysis module 108 isconfigured to generate a hierarchical clustering algorithm 124. A“hierarchical clustering algorithm” as used in this disclosure includesa method of cluster analysis that groups objects such as datapoints in away that datapoints assigned to the same cluster are deemed to be moresimilar to each other than datapoints found in other clusters.Hierarchical clustering algorithm 124 may include an agglomerative orbottom-up approach where each observation may start in its own clusterand pairs of clusters are merged as one moves up the hierarchy.Hierarchical clustering algorithm 124 may include divisive or a top-downapproach where all observations may start in one cluster and splits maybe performed recursively as one moves down the hierarchy. Beforeclustering is performed, linkage measurements must be performed thatdetermine a proximity matrix containing the distance between eachdatapoint using a distance function. The proximity matrix may then beupdated to display the distance between each cluster. Linkagemeasurements may include complete linkage, where for each pair ofclusters, the algorithm computes and merges them to minimize the maximumdistance between the clusters. Linkage measurements may include averagelinkage, where the algorithm may use the average distance between thepairs of clusters. Linkage measurements may include ward's linkage whereall clusters are considered, and the algorithm computes the sum ofsquared distances within the clusters and merges them to minimize it.Clusters that may be combined as in agglomerative hierarchicalclustering or clusters that may be split as in divisive hierarchicalclustering may be determined based on measuring dissimilarity betweensets of observations. Dissimilarity may be measured utilizing a metricthat may include but are not limited to Euclidean distance, squaredEuclidean distance, Manhattan distance, maximum distance, Hammingdistance, Levenshtein distance, and/or mahalanobis distance.Hierarchical clustering algorithm 124 may include assigning datapointsto separate clusters and then computing the distance or similaritybetween each of the clusters. Hierarchical clustering algorithm 124outputs a definite number of classified dataset clusters each containinga cluster label. Each cluster is distinct from each other cluster anddatapoints contained within each cluster are broadly similar to eachother. Each output cluster contains a cluster label, which identifiesthe topic of each cluster and datapoints contained within a cluster anddistinguish the clusters from each other. Cluster labels may begenerated from terms that occur frequently within the centroid of acluster. Cluster labels may be generated from title labels and/orexternal knowledge labels. Cluster labels may be displayed as textualdata, image data, and the like. In an embodiment, cluster labels ofseveral different cluster labelers may be combined to achieve definedand precise labels. For example, linear regression may be used to learnan optimal combination of labeler scores. In an embodiment, clusterlabel may indicate and describe a particular body dimension to beanalyzed. Body analysis module 108 assigns the plurality of user bodymeasurements to a first classified dataset cluster 128 containing acluster label. Body analysis module 108 selects a first assigned userbody data element containing the cluster label.

With continued reference to FIG. 1, system 100 includes a food analysismodule 132, which may be implemented as any hardware and/or softwaremodule. Food analysis module 132 is designed and configured to receivethe first assigned user body data element containing the cluster labelfrom the body analysis module 108; select a food training set 136 from afood database 140 as a function of the user body data element whereinthe food training set 136 correlates user body measurements to foodtolerance scores; generate using a supervised machine-learning process afood model 144 that receives the first assigned user body data elementas an input and produces an output containing a food tolerance score;generate a food tolerance instruction set 148 using the food tolerancescore; and display on a graphical user interface 152 located on theprocessor 104 the output containing the food tolerance instruction set148 containing the food tolerance score.

With continued reference to FIG. 1, food analysis module 132 selects afood training set 136 from a food database 140. Food training set is aset of training data. “Training data,” as used in this disclosure, isdata containing correlation that a machine-learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by at least a server may correlate any input data asdescribed in this disclosure to any output data as described in thisdisclosure.

With continued reference to FIG. 1, correlation in a training set mayinclude any relation established therein linking one datum to another,including inclusion together in a data element, row, column, cell, orthe like, and/or by giving each a common indicator and/or labelindicative of their correlation in data used to create and/or compiletraining data. Correlation may include a relation established wherebyuser body measurements are correlated to food tolerance scores based ondata entries obtained from the same subject. Training set may include aplurality of entries, each entry correlating at least an element of userbody measurement data to food tolerance scores.

With continued reference to FIG. 1, food training set includes aplurality of data entries. Food training set includes at least anelement of user body measurement correlated to food tolerance scores.For instance and without limitation, food training set may include auser body measurement such as a positive methane breath test correlatedto a food tolerance score that indicates avoiding foods that includehoney, agave nectar, lactose, wheat, apples, and pears and alsoindicates consuming superfoods such as buckwheat, kiwi, quinoa, millet,kale, chicken, and lamb and also indicates limiting foods that containgarlic and onion. In yet another non-limiting example, food training setmay include a user body measurement such as a positive copy of a MSH6 orLynch Syndrome gene correlated to a food tolerance score that indicatesavoiding foods such as sausage, red meat, margarine, and soda andindicates consuming superfoods that include broccoli, kale, Brusselsprouts, onion, garlic, and leek and also indicates limiting foods thatcontain white sugar and products synthesized from corn.

With continued reference to FIG. 1, food analysis module 132 may selecta food training set 136 from a food database 140. Food database 140 mayinclude any data structure suitable for use as clustering database 120as described above. Food database 140 may store food training set 136.Food analysis module 132 may select a food training set 136 from fooddatabase 140 as a function of user body data element wherein foodtraining set 136 correlates user body measurements to food tolerancescores. For instance and without limitation, user body data element mayinclude a blood sample containing an elevated hemoglobin A1C level. Insuch an instance, food analysis module 132 may select a food trainingset 136 from food database 140 containing a data entry that includes anelevated hemoglobin A1C level correlated to food tolerance scores. Inyet another non-limiting example, user body data element may include astool sample containing elevated levels of calprotectin released bywhite blood cells and linked to inflammation in the intestines. In suchan instance, food analysis module 132 may select a food training set 136from food database 140 containing a data entry that includes an elevatedcalprotectin level correlated to food tolerance scores.

With continued reference to FIG. 1, a “food tolerance score” as used inthis disclosure includes an indication of a user ability to tolerate aparticular food item. Tolerance includes a capacity of a human being todigest a particular food item. Food item may include any substancesuitable for consumption by a human being. For instance and withoutlimitation, food item may include a fruit such as banana, a vegetablesuch as kale, an animal product such as lamb, a protein such as tofu, anherb such as rosemary, a spice such as cinnamon and the like. Foodtolerance score may include an indicator generated on a curve oftolerability that may include a particular category of tolerability suchas food items that can be enjoyed without concern, food items thatshould be minimized and only consumed with regard to particular servingsizes, and food items that should be avoided due to an inability of thebody to metabolize them and/or the contribution of particular food itemsto contribute to inflammation and gut dysbiosis. Food tolerance scoresmay include a numerical score that may indicate tolerance to aparticular food item as reflected in a score between zero to onehundred, whereby a score with a higher value and closer to one hundredmay reflect higher tolerance.

With continued reference to FIG. 1, food analysis module 132 isconfigured to generate using a supervised machine-learning process afood model 144 that receives the first assigned user body data elementas an input and produces an output containing a food tolerance score.Food model 144 includes any machine learning process that is a linear orpolynomial regression algorithm, the food model might be an equation; itmight be a set of instructions to generate outputs based on inputs whichis derived using the machine-learning algorithm, and the like.Supervised machine-learning algorithms, as defined herein, includealgorithms that receive a training set relating a number of inputs to anumber of outputs, and seek to find one or more mathematical relationsrelating inputs to outputs, where each of the one or more mathematicalrelations is optimal according to some criterion specified to thealgorithm using some scoring function. For instance, a supervisedlearning algorithm may use elements of body measurements as inputs, foodtolerance scores as outputs, and a scoring function representing adesired form of relationship to be detected between elements of bodymeasurements and food tolerances; scoring function may, for instance,seek to maximize the probability that a given element of food toleranceis associated with a given food tolerance score and/or combination offood tolerance scores to minimize the probability that a given elementof body measurement data and/or combination of elements of bodymeasurement data is not associated with a given food tolerance scoreand/or combination of food tolerance scores. Scoring function may beexpressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in a training set. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various possiblevariations of supervised machine-learning algorithms that may be used todetermine relation between elements of body measurement data and foodtolerance scores. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain forinstance, a supervised machine-learning process may be performed withrespect to a given set of parameters and/or categories of parametersthat have been suspected to be related to a given set of bodymeasurements, and/or are specified as linked to a medical specialtyand/or field of medicine covering a particular set of body measurementdata. As a non-limiting example, a particular set of body measurementsthat indicate gut dysbiosis and candida overgrowth may be typicallyassociated with a known intolerance to fermented foods such assauerkraut, and a supervised machine-learning process may be performedto relate those body measurements to the various food intolerancescores; in an embodiment, domain restrictions of supervisedmachine-learning procedures may improve accuracy of resulting models byignoring artifacts in training data. Domain restrictions may besuggested by experts and/or deduced from known purposes for particularevaluations and/or known tests used to evaluate diagnostic data.Additional supervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between body measurement data and foodtolerance scores.

With continued reference to FIG. 1, food analysis module 132 isconfigured to generate a food tolerance instruction set 148 using a foodtolerance score. A “food tolerance instruction set 148” as used in thisdisclosure, includes a data structure containing one or more foodtolerance scores organized by particular categories of food items. Forinstance and without limitation, food tolerance scores may be organizedby particular food categories such as vegetables, proteins, fats,fruits, grains, herbs, spices, beverages, and the like. Food tolerancescores may be organized by particular tolerance scores such as all fooditems that a user can enjoy without hesitation, all food items that auser should minimize, and all food items that a user should avoid. Foodtolerance instruction set may include a numerical score containing atolerance percentage for each food item. For instance and withoutlimitation, food tolerance instruction set may include a numerical scoreon a scale from 1 to 100, with 1 being the least tolerable and 100 beingthe most tolerable. Each food item may contain a numerical scoreindicating a particular food tolerance level. For instance and withoutlimitation, a food instruction set may include a food item such as kalehaving a food tolerance score of 20% while a food item such as greenstring beans having a food tolerance score of 75%. In an embodiment,food tolerance instruction set may be displayed on a graphical userinterface using textual and/image representations. Food toleranceinstruction set displayed on a graphical user interface may allow a userto touch and select a particular food item to open in a new windowcontaining a photo of that particular food item with its own foodtolerance score for that particular user. In an embodiment, foodtolerance instruction set may be displayed on graphical user interfaceas pictures where a user can scroll through different pictures of fooditems and select a particular photograph of a food item to open in a newscreen and display more detailed information about a particular fooditem.

With continued reference to FIG. 1, food analysis module 132 isconfigured to receive a user entry containing a food tolerance aversioninput from a user client device 156 and filter the food toleranceinstruction set 148 as a function of the user entry. A “user foodtolerance aversion input” as used in this disclosure, includes a userinput containing one or more food items that a user has an aversion toand prefers not to consume. An aversion to a food item may be due to ananaphylactic reaction to one or more food items, a food sensitivity toone or more food items, an aversion to one or more food items, areligious practice to not consume one or more food items, a dislike toone or more food items, a customary practice to not consume one or morefood items, a dietary restriction to not consume one or more food itemsdue to one or more health conditions or medical issues and the like.User client device 156 may include without limitation, a display incommunication with processor 104; display may include any display asdescribed herein. User client device 156 may include an additionalcomputing device, such as a mobile device, laptop, desktop computer andthe like. Food analysis module 132 filters the food toleranceinstruction set 148 as a function of a user input. Filtering may includeremoving and/or limiting food items that contain a food toleranceaversion input within food tolerance instruction set 148.

With continued reference to FIG. 1, system 100 includes a graphical userinterface 152. Graphical user interface 152 may include withoutlimitation, a form or other graphical element having data entry fields,wherein a processor 104 may display data including for example foodtolerance instruction set 148 to a user and/or medical professional suchas a functional medicine doctor. Graphical user interface 152 mayinclude data entry fields that may allow a user and/or informed advisorto enter free form textual inputs. Graphical user interface 152 mayprovide drop-down lists where a user and/or medical professional may beable to select one or more entries contained within a particulardrop-down list. Food analysis module 132 displays on graphical userinterface 152 output containing food tolerance instruction set 148containing the food tolerance score. In an embodiment, a user and/ormedical professional viewing a food tolerance instruction set 148 mayselect an organization scheme for the food tolerance instruction set 148to be displayed on graphical user interface 152. Food toleranceinstruction set 148 may be organized according to any organizationalscheme as described above.

With continued reference to FIG. 1, system 100 includes a menu generatormodule 160, which may be implemented as any hardware and/or softwaremodule. Menu generator module 160 is designed and configured to receivethe food tolerance instruction set 148 from the food analysis module132; select a menu training set 164 from a menu database 168 as afunction of the food tolerance instruction set 148 wherein the menutraining set 164 correlates food tolerance scores to menu options 176;generate using a supervised machine-learning process a menu model 172that receives the food tolerance instruction set 148 as an input andproduces an output containing menu options 176 utilizing the menutraining set 164; and display on the graphical user interface 152located on the processor 104 the output containing menu options 176.

With continued reference to FIG. 1, menu generator module 160 selects amenu training set 164 from a menu database 168. Menu training set 164correlates food tolerance scores to menu options 176. A “menu option” asused in this disclosure, includes a list of available dish choices thatmay be selected for a particular meal. A meal may include an eatingoccasion that occurs at certain time such as breakfast, lunch, dinner,and/or snacks. Menu option may include a description of food items thatmay be incorporated into particular dish choices. Dish choices includeparticular dishes that may be prepared with particular and distinctiveingredients and techniques. For instance and without limitation, a dishchoice for breakfast may include an option such as oatmeal topped withwalnuts and blueberries. In yet another non-limiting example, a dishchoice for lunch may include an option such as a salad containingromaine lettuce and topped with turkey breast and avocado. Menu database168 includes any data structure suitable for use as clustering database120 as described above. Menu generator module 160 selects a menutraining set 164 from menu database 168 as a function of food toleranceinstruction set 148. For instance and without limitation, menu generatormodule 160 may select a menu training set 164 that includes a data entrycontaining a food tolerance for a vegetable such as spinach correlatedto a menu option that includes a spinach salad if a food toleranceinstruction set 148 contains spinach having a tolerance score of enjoywhere a user is able to consume spinach without any resultinggastrointestinal symptoms. Menu generator module 160 may not select amenu training set 164 that includes a data entry containing a foodtolerance for a fruit such as apricot correlated to a menu option thatincludes a fruit salad containing apricot if a food toleranceinstruction set 148 contains apricot having a tolerance score ofminimize or avoid.

With continued reference to FIG. 1, menu generator module 160 generatesusing a supervised machine-learning process a menu model 172 thatreceives the food tolerance instruction set 148 as an input and producesan output containing menu options 176 utilizing the menu training set164. Menu model 172 includes any machine learning process that is alinear or polynomial regression algorithm, the menu model might be anequation; it might be a set of instructions to generate outputs based oninputs which is derived using the machine-learning algorithm, and thelike. Supervised machine-learning process may include any of thesupervised machine-learning processes as described above. Menu generatormodule 160 displays on graphical user interface 152 output containingmenu options 176. Graphical user interface 152 includes any of thegraphical user interface 152 as described above.

With continued reference to FIG. 1, system 100 includes a local selectormodule 180, which may be implemented as any hardware and/or softwaremodule. Local selector module 180 is designed and configured to receivea plurality of meal option inputs 184 from a meal preparer device 188wherein the meal option inputs 184 contain available menu listings;receive the output containing menu options 176 from the menu generatormodule 160; generate a k-nearest neighbors algorithm 192 utilizing theplurality of meal option inputs 184 and the menu options 176; identify aplurality of compatible meal options 196 as a function of generating thek-nearest neighbor algorithm; and display the plurality of compatiblemeal options 196 on the graphical user interface 152 located on theprocessor 104.

With continued reference to FIG. 1, local selector module 180 isconfigured to receive a plurality of meal option inputs 184 from a mealpreparer device 188 wherein the meal option inputs 184 contain availablemenu listings. A “meal option input” as used in this disclosure,includes data describing available meals that a user can order andpurchase that are cooked and prepared from meal preparers. “Mealpreparers” as used in this disclosure, include any participant involvedin the preparation and/or cooking of meal options. Meal providers mayinclude a restaurant such as a locally owned independently operatingrestaurant or a chain restaurant that is found at multiple locations.Meal providers may include a company that prepares pre-packaged meals.Meal providers may include a grocery store that prepares meals. Mealpreparers may include restaurants located within grocery stores. Mealpreparers may include chefs or cooks who prepare meals at home or in aprivate commercialized kitchen. Meal providers may include a chef orcook who prepares meals in a school or kitchen or space that the chef orcook rents out for example. Meal option inputs 184 include availablemenu listings. “Available menu listings” as used in this disclosure,include one or more dishes that a meal preparer has available forpurchase by a user. Available menu listings may include one or moredishes for a particular meal such as breakfast, lunch, dinner, and/orsnacks or may contain available items that can be purchased irrespectiveof what meal they relate to. For instance and without limitation, anavailable menu listing may contain an option such as yogurt parfait withberries for breakfast, seared salmon with asparagus for lunch, andshrimp scampi for dinner. Available menu listing may contain one or moredishes available for a particular meal, such as for breakfast availablemenu listing may include buckwheat pancakes, Mediterranean omelet, berrysmoothie, or oatmeal topped with shredded coconut and flax seeds.

With continued reference to FIG. 1, local selector module 180 receives aplurality of meal option inputs 184 from a meal preparer device 188.Meal preparer device 188 may include any device suitable for use as userclient device 156 as described above.

With continued reference to FIG. 1, local selector module 180 generatesa k-nearest neighbor algorithm utilizing the plurality of meal optioninputs 184 and the menu options 176. “K-nearest neighbor algorithm” asused in this disclosure, includes a lazy-learning method that utilizesfeature similarity to analyze how closely out of sample featuresresemble training data to locate possible optimal vector outputs,classify possible optimal vector outputs, calculate an optimal vectoroutput, and generate an optimal vector output. Generating a k-nearestneighbor algorithm utilizing a k-nearest neighbor algorithm may includespecifying a K-value, selecting k entries in a database which areclosest to the known sample, determining the most common classifier ofthe entries in the database, and classifying the known sample. Alazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call when needed” process and/orprotocol, may be a process whereby machine-learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship. As a non-limiting example, an initialheuristic may include a ranking of associations between inputs andelements of training data. Heuristic may include selecting some numberof highest-ranking associations and/or training data elements. Lazylearning may implement any suitable lazy learning algorithm, includingwithout limitation, a K-nearest neighbors algorithm 192, a lazy naïveBayes algorithm, or the like; persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of the variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below. Generating k-nearest neighbor algorithm identifies aplurality of compatible meal options 196, which are displayed on agraphical user interface 152 located on a processor 104.

With continued reference to FIG. 1, local selector module 180 isconfigured to select a compatible meal options 196 from the plurality ofcompatible meal options 196. Local selector module 180 receives a userinput from a user client device 156 containing a meal option elementindicator. A “user meal option element indicator” as used in thisdisclosure, includes data describing one or more user preferencesregarding compatible meal options 196. User preferences may includeinformation such as how much money a user wishes to spend on aparticular compatible meal option, how many meals a user may beordering, certain restrictions a user may have as to particular sourcesof ingredients that may be utilized to prepare a meal option such as apreference for organic produce or free range meats, user habits such ashow frequently a user consumes meals throughout the day, quality ofingredients that may be utilized such as cold pressed olive oil ornon-genetically modified corn products and the like. Local selectormodule 180 generates a loss function utilizing the user input and theplurality of meal option inputs 184.

With continued reference to FIG. 1, mathematical expression mayrepresent a loss function, where a “loss function” is an expression anoutput of which an optimization algorithm minimizes to generate anoptimal result. As a non-limiting example, local selector module 180 maycalculate variables reflecting scores relating to particular user mealoption element indicators, calculate an output of mathematicalexpression using the variables, and select a meal option that producesan output having the lowest size, according to a given definition of“size,” of the set of outputs representing each of the plurality of mealoptions; size may, for instance, included absolute value, numericalsize, or the like. Selection of different loss functions may result inidentification of different meal options as generating minimal outputs;for instance, where organic ingredients is associated in a first lossfunction with a large coefficient or weight, a meal option having asmall coefficient or weight for organic ingredients may minimize thefirst loss function, whereas a second loss function wherein organicingredients has a smaller coefficient but degree of variance from costwhich has a larger coefficient may produce a minimal output for adifferent meal option having a larger organic ingredients but moreclosely hewing to cost.

With continued reference to FIG. 1, mathematical expression and/or lossfunction may be generated using a machine learning to produce lossfunction: i.e., regression. Mathematical expression and/or loss functionbe user-specific, using a training set composed of past user selections;may be updated continuously. Mathematical expression and/or lossfunction may initially be seeded using one or more user inputs as above.User may enter a new command changing mathematical expression, and thensubsequent user inputs may be used to generate a new training set tomodify the new expression.

With continued reference to FIG. 1, mathematical expression and/or lossfunction may be generated using machine learning using a multi-usertraining set. Training set may be created using data of a cohort ofpersons having similar demographic, religious, health, and/or lifestylecharacteristics to user. This may alternatively or additionally be usedto seed a mathematical expression and/or loss function for a user, whichmay be modified by further machine learning and/or regression usingsubsequent user selections of alimentary provision options. Lossfunction analysis may measure changes in predicted values versus actualvalues, known as loss or error. Loss function analysis may utilizegradient descent to learn the gradient or direction that a cost analysisshould take in order to reduce errors. Loss function analysis algorithmsmay iterate to gradually converge towards a minimum where further tweaksto the parameters produce little or zero changes in the loss orconvergence by optimizing weights utilized by machine learningalgorithms. Loss function analysis may examine the cost of thedifference between estimated values, to calculate the difference betweenhypothetical and real values. Local selector module 180 may utilizevariables to model relationships between past interactions between auser and system 100 and compatible meal options 196. In an embodimentloss function analysis may utilize variables that may impact userinteractions and/or compatible meal options 196. Loss function analysismay be user specific so as to create algorithms and outputs that arecustomize to variables for an individual user.

With continued reference to FIG. 1, local selector module 180 isconfigured to generate a k-means clustering algorithm utilizing theplurality of meal option inputs 184 and the menu options 176 andidentify a compatible meal option as a function of generating thek-means clustering algorithm. “K-means clustering algorithm” as used inthis disclosure, includes cluster analysis that partitions nobservations or unclassified cluster data entries into k clusters inwhich each observation or unclassified cluster data entry belongs to thecluster with the nearest mean. Cluster data entry may include dataentries selected from a clustering dataset. Cluster data entry may bereceived from a clustering database 120. “Cluster analysis” as used inthis disclosure, includes grouping a set of observations or data entriesin way that observations or data entries in the same group or clusterare more similar to each other than to those in other groups orclusters. Cluster analysis may be performed by various cluster modelsthat include connectivity models such as hierarchical clustering,centroid models such as k-means, distribution models such asmultivariate normal distribution, density models such as density-basedspatial clustering of applications with nose (DBSCAN) and orderingpoints to identify the clustering structure (OPTICS), subspace modelssuch as biclustering, group models, graph-based models such as a clique,signed graph models, neural models, and the like. Cluster analysis mayinclude hard clustering whereby each observation or unclassified clusterdata entry belongs to a cluster or not. Cluster analysis may includesoft clustering or fuzzy clustering whereby each observation orunclassified cluster data entry belongs to each cluster to a certaindegree such as for example a likelihood of belonging to a cluster.Cluster analysis may include strict partitioning clustering whereby eachobservation or unclassified cluster data entry belongs to exactly onecluster. Cluster analysis may include strict partitioning clusteringwith outliers whereby observations or unclassified cluster data entriesmay belong to no cluster and may be considered outliers. Clusteranalysis may include overlapping clustering whereby observations orunclassified cluster data entries may belong to more than one cluster.Cluster analysis may include hierarchical clustering wherebyobservations or unclassified cluster data entries that belong to a childcluster also belong to a parent cluster.

With continued reference to FIG. 1, local selector module 180 may selecta specific number of groups or clusters to output, identified by thevariable “k.” Generating a k-means clustering algorithm includesassigning inputs containing unclassified data to a “k-group” or“k-cluster” based on feature similarity. Centroids of k-groups ork-clusters may be utilized to generate classified data entry clusters.Local selector module 180 by select “k” variable by calculating k-meansclustering algorithm for a range of k values and comparing results.Local selector module 180 may compare results across different values ofk as the mean distance between cluster data entries and clustercentroid. Local selector module 180 may calculate mean distance to acentroid as a function of k value, and the location of where the rate ofdecrease starts to sharply shift, this may be utilized to select a kvalue. Centroids of k-groups or k-cluster include a collection offeature values which are utilized to classify data entry clusterscontaining cluster data entries. Generating a k-means clusteringalgorithm includes generating initial estimates for k centroids whichmay be randomly generated or randomly selected from unclassified datainput. K centroids may be utilized to define one or more clusters. Localselector module 180 may assign unclassified data to one or morek-centroids based on the squared Euclidean distance by first performinga data assigned step of unclassified data. Local selector module 180 mayassign unclassified data to its nearest centroid based on the collectionof centroids ci of centroids in set C. Unclassified data may be assignedto a cluster based on argmin_(ci)

_(C) dist(ci,x)², where argmin includes argument of the minimum; ciincludes a collection of centroids in a set C; and dist includesstandard Euclidean distance. Local selector module 180 may thenrecompute centroids by taking mean of all cluster data entries assignedto a centroid's cluster. This may be calculated based onci=1/|Si|Σxi∈Si^(xi). Local selector module 180 may continue to repeatthese calculations until a stopping criterion has been satisfied such aswhen cluster data entries do not change clusters, the sum of thedistances have been minimized, and/or some maximum number of iterationshas been reached. K-means clustering algorithm identifies a compatiblemeal option as a function of generating k-means clustering algorithm.

With continued reference to FIG. 1, local selector module displays theplurality of compatible meal options on a graphical user interface. A“compatible meal option” as used in this disclosure, includes any mealthat accommodates a user's biological markers. Accommodates includesgenerating and selecting meal options that are based on a user'sbiological markers and body measurements as described throughout thisdisclosure. A compatible meal option contains food items that areconsidered superfoods, food items that can be enjoyed, as well as fooditems that should be minimized from a food tolerance instruction set. Acompatible meal option does not contain food items that are consideredfood items to be avoided from a food tolerance instruction set.

Referring now to FIG. 2, an exemplary embodiment 200 of biologicalmarker database 116 is illustrated. Biological marker database 116 maybe implemented as any data structure suitable for use as clusteringdatabase 120 as described above in reference to FIG. 1. Biologicalmarker database 116 may store one or more biological markers 112. One ormore tables contained within biological marker database 116 may includemicrobiome sample table 204; microbiome sample table 204 may store oneor more biological marker 112 relating to the microbiome. For instanceand without limitation, microbiome sample table 204 may include resultsreflecting levels of a particular bacterial strain such as quantities ofBifidobacterium found in a user's gastrointestinal tract. One or moretables contained within biological marker database 116 may include fluidsample table 208; fluid sample table 208 may store one or morebiological marker 112 obtained from a fluid sample. For instance andwithout limitation, fluid sample table 208 may include one or moreentries containing results from fluids such as urine, saliva, sweat,tears, blood, mucus, cerebrospinal fluid, and the like analyzed for oneor more biological marker 112. One or more tables contained withinbiological marker database 116 may include sensor data table 212; sensordata table 212 may include one or more biological marker 112 obtainedfrom one or more sensors. For instance and without limitation, sensordata table 212 may include sleeping patterns of a user recorded by asensor. One or more tables contained within biological marker database116 may include genetic sample table 216; genetic sample table 216 mayinclude one or more biological marker 112 containing one or more geneticsequences. For instance and without limitation, genetic sample table 216may include a user's genetic sequence for a particular gene such as asequence illustrating a positive breast cancer one (BRACA 1) gene. Oneor more tables contained within biological marker database 116 mayinclude stool sample table 220; stool sample table 220 may include oneor more biological marker 112 obtained from a stool sample. For instanceand without limitation, stool sample table 220 may include a user'sstool sample analyzed for the presence and/or absence of one or moreparasites. One or more tables contained within biological markerdatabase 116 may include tissue sample table 224; tissue sample table224 may include one or more biological marker 112 obtained from one ormore tissue samples. For instance and without limitation, tissue sampletable 224 may include a breast tissue sample analyzed for the absenceand/or presence of estrogen markers. Other tables not illustrated mayinclude but are not limited to epigenetic, gut-wall, nutrients, and/ormetabolism.

Referring now to FIG. 3, an exemplary embodiment 300 of body analysismodule 108 is illustrated. Body analysis module 108 may be implementedas any software and/or hardware module. Body analysis module 108receives a user biological marker 112 containing a plurality of userbody measurements. Body analysis module 108 may receive a userbiological marker 112 from user client device 156. This may be performedutilizing any network methodology as described herein. Body analysismodule 108 may receive a user biological marker 112 from biologicalmarker database 116 as described above in more detail in reference toFIG. 2. For instance and without limitation, body analysis module 108may receive from biological marker database 116 a biological marker 112such as a saliva sample analyzed for multiple user body measurementssuch as progesterone level, testosterone level, estrogen level, heavymetal toxicity, cortisol measurement, and thyroid level. In yet anothernon-limiting example, body analysis module 108 may receive from userclient device 156 a user biological marker 112 such as results from astool sample analyzed for multiple user body measurements includingdigestion markers such as chymotrypsin, putrefactive short-chain fattyacids, gut metabolic markers such as beneficial short-chain fatty acidswith n-butyrate, beta-glucuronidase, fecal lactoferrin, and gutmicrobiology markers such as species of beneficial bacteria, additionalbacteria, and mycology. In an embodiment, user body measurements includean element of user microbiome data, an element of user gut-wall data,and an element of user genetic data. For instance and withoutlimitation, body analysis module 108 may receive a user biologicalmarker 112 obtained from a finger prick blood sample that includes bodymeasurements containing an element of user microbiome data such as ananalysis of pathogenic bacterial found within the blood sample, anelement of user gut-wall data such as a measurement of endotoxinlipopolysaccharide (LPS) found within the blood sample, and an elementof user genetic data such as a reading of a user's level of DNA repairmetabolites such as 8-OHdG levels.

With continued reference to FIG. 3, body analysis module 108 isconfigured to select a clustering dataset 304 from clustering database120. Clustering database 120 may be implemented as any data structure asdescribed above in more detail in reference to FIG. 1. Clusteringdataset 304 includes a plurality of unclassified datapoints. Clusteringdataset 304 may be stored in any suitable data and/or data type. Forinstance and without limitation, clustering dataset may include textualdata such as numerical, character, and/or string data. Textual data mayinclude a standardized name and/or code for a disease, disorder, or thelike; codes may include diagnostic codes and/or diagnosis codes, whichmay include without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as dataset may take; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various forms which may be suitable for use as datasetconsistently with this disclosure.

With continued reference to FIG. 3, clustering dataset 304 may be storedas image data, such as for example an image of a particular foodsubstance such as a photograph of a pear or an image of a steak. Imagedata may be stored in various forms including for example, jointphotographic experts group (JPEG), exchangeable image file format(Exif), tagged image file format (TIFF), graphics interchange format(GIF), portable network graphics (PNG), netpbm format, portable bitmap(PBM), portable any map (PNM), high efficiency image file format (HEIF),still picture interchange file format (SPIFF), better portable graphics(BPG), drawn filed, enhanced compression wavelet (ECW), flexible imagetransport system (FITS), free lossless image format (FLIF), graphicsenvironment manage (GEM), portable arbitrary map (PAM), personalcomputer exchange (PCX), progressive graphics file (PGF), gerberformats, 2 dimensional vector formats, 3 dimensional vector formats,compound formats including both pixel and vector data such asencapsulated postscript (EPS), portable document format (PDF), andstereo formats.

With continued reference to FIG. 3, datasets may be obtained from aplurality of sources. Clustering datasets 304 contained withinclustering database 120 may contain a plurality of data entries,obtained for example, from patient medical records that have beenstripped of identifying information. Clustering datasets 304 containedwithin clustering database 120 may be obtained from patient surveys whomay be sampled in a variety of methods such as by phone, mail, internetand the like. Patient surveys may be distributed to patients across abreadth of geographical locations and may also be stripped ofidentifying information. Clustering datasets contained within clusteringdatabase 120 may be obtained from clinical data such as from facilitiesincluding nursing homes, hospitals, home health agencies, and the like.

With continued reference to FIG. 3, clustering dataset 304 contains aplurality of unclassified datapoints. “Unclassified datapoints” as usedin this disclosure, include datapoints that have not been assigned,generated, and/or calculated category labels. Classification may includethe process of predicting a class of given data entries. Classificationmay include using predictive modeling that approximates a mappingfunction from input variables to discrete output variables.Classification may be performed utilizing classification algorithms thatinclude for example decision trees, naïve bayes, artificial neuralnetworks, boosting, kernel methods, and/or k-nearest neighbors algorithm192.

With continued reference to FIG. 3, body analysis module 108 may selecta clustering dataset 304 by classifying a user body data element to abody dimension. Body dimension may include any of the body dimensions asdescribed above in reference to FIG. 1. Body analysis module 108 mayclassify biological marker 112 to a body measurement utilizing any ofthe classification models as described above. Body analysis module 108generates a classification label containing a body dimension label. Forinstance and without limitation, a biological marker 112 such as a bloodsample analyzed for intracellular nutrient levels that include sodium,calcium, and potassium may be classified to contain a body dimensionlabel that contains nutrients. Body analysis module 108 selects aclustering dataset from clustering database 120 by matching a bodydimension label to a clustering dataset containing unclassifieddatapoints relating to the body dimension. For instance and withoutlimitation, a body dimension label such as gut-wall integrity may bematched to a clustering dataset that contains unclassified datapointsthat include measurements of markers of gut-wall integrity. In yetanother non-limiting example, a body dimension label such as microbiomemay be matched to a clustering dataset that contains unclassifieddatapoints that include measurements of the microbiome.

With continued reference to FIG. 3, body analysis module 108 may includehierarchical clustering module 308, which may be implemented as anyhardware and/or software module. Hierarchical clustering module 308 isconfigured to generate a hierarchical clustering algorithm 124 using theclustering dataset and input. Hierarchical clustering algorithm 124 mayinclude any of the hierarchical clustering algorithm 124 as describedabove in reference to FIG. 1. Hierarchical clustering module 308 outputsa definite number of classified dataset clusters each containing aclustering label. Clusters may be composed of one or more datapointsthat have one or more shared similarities. A “clustering label” as usedin this disclosure, includes descriptive labels for each clusterproduced by a clustering algorithm. Clustering labels may be generatedby calculating one or more algorithms that may produce an output thatsummarizes the topic of each clustering and distinguishes it from everyother cluster. Clustering labels may be generated to reflect the one ormore shared similarities of each datapoint contained within a particularcluster.

With continued reference to FIG. 3, body analysis module 108 may includeclassified dataset clustering module 312, which may be implemented asany hardware and/or software module. Classified dataset clusteringmodule 312 may assign the plurality of user body measurements to a firstclassified dataset cluster 128 containing a cluster label. Classifieddataset clustering module selects the first classified dataset cluster128 containing the cluster label. Body analysis module 108 is configuredto generate cluster labels indicating body dimension that datapointsrelate to. Body analysis module 108 receives a plurality of user bodymeasurements, generates a clustering algorithm using the user bodymeasurements as input and outputting a plurality of cluster labelscontaining a body dimension. Clustering algorithm may include anyclustering algorithm such as centroid based clustering, density basedclustering, distribution based clustering, hierarchical clustering,k-means clustering, mean-shift clustering, density-based spatialclustering of applications with noise, expectation-maximizationclustering using Gaussian mixture models, agglomerative hierarchicalclustering, and the like. Body analysis module 108 selects a firstclassified dataset cluster 128 as a function of the body dimension. Inan embodiment, expert input may be provided and stored within system 100that may rank body dimensions by importance and indicates what bodydimension needs to be addressed first. For instance and withoutlimitation, expert input may dictate that a body dimension such asmicrobiome needs to be selected and addressed first before gut-wallintegrity. In yet another non-limiting example, expert input may dictatethat a body dimension such as nutrients needs to be selected andaddressed first before metabolism.

Referring now to FIG. 4, an exemplary embodiment 400 of clusteringdatabase 120 is illustrated. Clustering database 120 may be implementedas any data structure as described above. Clustering database 120 maystore one or more clustering datasets, which may be organized accordingto datapoints contained within each dataset. One or more tablescontained within clustering database 120 may include microbiome clustertable 404; microbiome cluster table 404 may include one or moreclustering datasets related to the microbiome body dimension. One ormore tables contained within clustering database 120 may includeepigenetic cluster table 408; epigenetic cluster table 408 may includeone or more datasets related to the epigenetic body dimension. One ormore tables contained within clustering database 120 may include geneticcluster table 412; genetic cluster table 412 may include one or moredatasets related to genetic body dimension. One or more tables containedwithin clustering database 120 may include gut-wall cluster table 416;gut-wall cluster table 416 may include one or more datasets related togut-wall body dimension. One or more tables contained within clusteringdatabase 120 may include stool sample cluster table 420; stool samplecluster table 420 may include one or more datasets containing datapointsobtained from stool samples. One or more tables contained withinclustering database 120 may include tissue sample cluster table 424;tissue sample cluster table 424 may include one or more datasetscontaining datapoints obtained from tissue samples. Other tables notillustrated may include but are not limited to nutrients cluster table,metabolism cluster table, fluid sample cluster table, and/or sensorcluster table.

Referring now to FIG. 5, an exemplary embodiment 500 of expert knowledgedatabase is illustrated. Expert knowledge database may be implemented asany data structure as described above in reference to FIG. 1. One ormore database tables may be linked to one another by, for instance,common column values. For instance, a common column between two tablesof expert knowledge database may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data may be included in one or more tables.

With continued reference to FIG. 5, expert knowledge database includes aforms processing module 504 that may sort data entered in a submissionvia graphical user interface 152 by, for instance, sorting data fromentries in the graphical user interface 152 to related categories ofdata; for instance, data entered in an entry relating in the graphicaluser interface 152 to a body dimension may be sorted into variablesand/or data structures for storage of body dimensions, while dataentered in an entry relating to a category of clustering data and/or anelement thereof may be sorted into variables and/or data structures forthe storage of, respectively, categories of clustering data. Where datais chosen by an expert from pre-selected entries such as drop-downlists, data may be stored directly; where data is entered in textualform, language processing module 508 may be used to map data to anappropriate existing label, for instance using a vector similarity testor other synonym-sensitive language processing test to map physiologicaldata to an existing label. Alternatively or additionally, when alanguage processing algorithm, such as vector similarity comparison,indicates that an entry is not a synonym of an existing label, languageprocessing module 508 may indicate that entry should be treated asrelating to a new label; this may be determined by, e.g., comparison toa threshold number of cosine similarity and/or other geometric measuresof vector similarity of the entered text to a nearest existent label,and determination that a degree of similarity falls below the thresholdnumber and/or a degree of dissimilarity falls above the thresholdnumber. Data from expert textual submissions 512, such as accomplishedby filling out a paper or PDF form and/or submitting narrativeinformation, may likewise be processed using language processing module508. Data may be extracted from expert papers 516, which may includewithout limitation publications in medical and/or scientific journals,by language processing module 508 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure.

With continued reference to FIG. 5, one or more tables contained withinexpert knowledge database may include expert body dimension table 520;expert body dimension table 520 may include one or more data entriescontaining expert input regarding body dimensions. One or more tablescontained within expert knowledge database may include expert foodtolerance table 524; expert food tolerance table 524 may include one ormore data entries containing expert input regarding food tolerancescores. One or more tables contained within expert knowledge databasemay include expert clustering table 528; expert clustering table 528 mayinclude one or more data entries containing expert input regardingclustering datasets, clustering algorithms, clustering datapoints, andthe like. One or more tables contained within expert knowledge databasemay include expert biological marker 112 table 532; expert biologicalmarker 112 table 532 may include one or more data entries containingexpert input regarding biological marker 112. One or more tablescontained within expert knowledge database may include expert menu table336; expert menu table 336 may include one or more data entriescontaining expert input regarding menus. One or more tables containedwithin expert knowledge database may include expert body measurementtable 340; expert body measurement table 340 may include one or moredata entries containing expert input regarding body measurements.

Referring now to FIG. 6, an exemplary embodiment 600 of food analysismodule 132 is illustrated. Food analysis module 132 may be implementedas any hardware and/or software module. Food analysis module 132receives from body analysis module 108 the first classified datasetcluster 128 containing a cluster label and a user biological marker 112.Food analysis module 132 may receive a first classified data set clusterand a user biological marker 112 utilizing any network topography asdescribed herein. Food analysis module 132 selects a food training set136 from a food database 140 as a function of a user biological marker112 wherein the food training set 136 correlates user body measurementsto food tolerance scores. In an embodiment, food training set 136 may beorganized within food training database by biological marker 112 asdescribed in more detail below. Food analysis module 132 may select foodtraining set 136 by matching a user biological marker 112 to a foodtraining ser containing data entries that contain the same userbiological marker 112 correlated to food tolerance scores. For instanceand without limitation, food analysis module 132 may receive a userbiological marker 112 that contains a body measurement such as asalivary estradiol level. In such an instance, food analysis module 132may select a food training set 136 that includes a body measurement thatincludes a salivary estradiol level correlated to food tolerance scores.In an embodiment, food training set 136 may be organized according tobody measurement and/or biological marker 112 as described in moredetail below.

With continued reference to FIG. 6, food analysis module 132 may includea supervised machine-learning module 604, which may be implemented asany hardware and/or software module. Supervised machine-learning module604 generates using a supervised machine-learning process a food model144 that receives the plurality of user body measurements assigned to afirst classified dataset cluster 128 containing a cluster label as aninput and produces an output containing a food tolerance score.Supervised machine-learning processes may include any of the supervisedmachine-learning models as described above in reference to FIG. 1. Foodtolerance score includes an indication of a user ability to tolerate aparticular food item. For instance and without limitation, a supervisedmachine-learning process may be utilized to produce an output thatcontains food tolerance score for a user with a biological marker 112such as breast cancer gene 2 (BRACA2) to contain a food tolerance scorethat includes foods a user can enjoy that are rich in glucosinolatessuch as broccoli and cauliflower, while food tolerance scores mayindicate foods the user can avoid as being foods rich in trans-fats suchas vegetable shortening, vegetable oil, fried foods, and cannedfrosting. In an embodiment, supervised-machine learning model may selecta food model 144 from food database 140. Food model 144 may bepre-calculated and loaded into food database 140.

With continued reference to FIG. 6, food analysis module 132 may includefood tolerance instruction set module 608, which may be implemented asany hardware and/or software module. Food tolerance instruction setmodule 608 generates a food tolerance instruction set 148 using the foodtolerance scores. Food tolerance instruction set 148 includes datastructure containing one or more food tolerance scores organized byparticular categories of food items as described above in reference toFIG. 1. For instance and without limitation, food tolerance instructionset 148 may include food items categorized into categories that mayinclude superfoods which contains food items that are the mostbeneficial, food items that can be enjoyed as frequently as a userdesires, food items that can be consumed with particular customizedlimits, and foods that should be avoided completely. In yet anothernon-limiting example, food tolerance instruction set 148 may becategorized by groups of food items, such as food items classified asvegetables, food items classified as proteins, food items classified asfats, food items classified as fruits, food items classified as grains,food items classified as herbs, food items classified as spices, andfood items classified as miscellaneous. Food analysis module 132displays on a graphical user interface 152 located on a processor 104the output produced by the food analysis module 132 containing the foodtolerance instruction set 148 containing the food tolerance score. In anembodiment, a user may indicate and/or select on a graphical userinterface 152 how a user wishes to view and have food items organizedwithin a particular food tolerance instruction set 148. Food analysismodule 132 is configured to receive a user entry from a user clientdevice 156 containing a user food tolerance aversion input and filter afood tolerance instruction set 148 as a function of the user entry. Auser food tolerance aversion may include a user input containing one ormore food items that a user has an aversion to and prefers not toconsume as described above in more detail in reference to FIG. 1. Forinstance and without limitation, a user food tolerance aversion inputmay include a user response that includes avoidance of blueberries andstrawberries because a user does not like the taste of certain berries.In yet another non-limiting example, a user food tolerance aversioninput may include a user response that includes avoidance of egg yolkand egg whites because user has a food sensitivity to egg products. Inyet another non-limiting example, a user food tolerance aversion inputmay include a user response that includes avoidance of walnuts becauseuser has a previously diagnosed immunoglobulin E (IGE) anaphylacticresponse to walnuts. In an embodiment, user food tolerance aversions maybe stored locally in a database and may be retrieved from the databaseby food analysis module 132. Food analysis module 132 may filter foodtolerance instruction set 148 by eliminating food items containing auser food tolerance aversion input from food tolerance instruction set148. Filtering by food analysis module 132 may also include changing afood item containing a user food tolerance aversion to contain a foodtolerance score to avoid.

Referring now to FIG. 7, an exemplary embodiment 700 of food database140 is illustrated. Food database 140 may be implemented as any datastructure as described above in reference to FIG. 1. In an embodiment,one or more tables contained within food database 140 may be organizedby body measurement. One or more tables contained within food database140 may include extracellular blood nutrient table 704; extracellularnutrient table 704 may include one or more training sets containing bodymeasurements that include but are not limited to extracellular bloodnutrients correlated to food tolerance scores. One or more tablescontained within food database 140 may include salivary progesteronetable 708; salivary progesterone table 708 may include one or moretraining sets containing body measurements that include but are notlimited to salivary progesterone levels correlated to food tolerancescores. One or more tables contained within food database 140 mayinclude sensor heart rate table 712; sensor heart rate table 712 mayinclude one or more training sets containing body measurements thatinclude but are not limited to sensor heart rates correlated to foodtolerance scores. One or more tables contained within food database 140may include APOE4 gene sequence table 716; APOE4 gene sequence table 716may include one or more training sets containing body measurements thatinclude but are not limited to APOE4 gene sequences correlated to foodtolerance scores. One or more tables contained within food database 140may include stool lactoferrin table 720; stool lactoferrin table 720 mayinclude one or more training sets containing body measurements thatinclude but are not limited to stool levels of lactoferrin correlated tofood tolerance scores. One or more tables contained within food database140 may include food model 144 table 724; food model 144 table 724 mayinclude one or more food model 144.

Referring now to FIG. 8, an exemplary embodiment 800 of menu generatormodule 160 is illustrated. Menu generator module 160 may be implementedas any hardware and/or software module. Menu generator module 160receives a food tolerance instruction set 148 from food analysis module132. Menu generator module 160 may receive a food tolerance instructionset 148 utilizing any hardware and/or software module. Menu generatormodule 160 selects a menu training set 164 from a menu database 168 as afunction of a food tolerance instruction set 148 wherein the menutraining set 164 correlates food tolerance scores to menu options 176.Menu generator module 160 may include menu learner 804 which may beimplemented as any hardware and/or software module. Menu learner 804 mayaid menu generator module 160 in selecting menu training set 164 frommenu database 168. Menu learner 804 is configured to receive a modeltraining set correlating food tolerance instruction set 148 to menutraining set 164 and generate using a supervised machine-learningprocess and the model training set a training set model that receivesthe food tolerance instruction set 148 as an input and produces anoutput containing menu training set 164. Model training set includes anytraining set as described herein. Training set model includes anymachine-learning model, including any supervised machine-learningprocess as described above. Menu learner 804 may learn and continue toupdate selections of menu training set 164 for particular food toleranceinstruction set 148. Menu learner may generate using a supervisedmachine-learning process and the model training set a training setmodel. Supervised machine-learning process may include any of thesupervised machine-learning processes as described above in reference toFIG. 1. Model training set and/or training set model may be storedwithin menu database 168. Training set models may be precalculated andloaded into menu database 168.

With continued reference to FIG. 8, menu generator module 160 mayinclude a lazy-learning module 808 which may be implemented as anyhardware and/or software module. Lazy-learning module 808 may beutilized to select a menu training set 164 by executing a lazy learningprocess as a function of a model training set and a food toleranceinstruction set 148 and producing an output containing menu training set164. A lazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover a “first guess” at a menu training set 164 associatedwith a food tolerance instruction set 148, using model training set. Asa non-limiting example, an initial heuristic may include a ranking ofmenu training set 164 according to relation to a test type of a foodtolerance instruction set 148, one or more categories of food itemsidentified in test type of a food tolerance instruction set 148, and/orone or more values detected in a food tolerance instruction set 148;ranking may include, without limitation, ranking according tosignificance scores of associations between food items and foodtolerance instruction set 148, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or food tolerance scores. Lazy learning module mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm192, a lazy naïve Bayes algorithm, or the like; persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious lazy-learning algorithms that may be applied to generate menutraining set 164 as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms.

With continued reference to FIG. 8, menu generator module 160 mayinclude supervised machine-learning module 812. Supervisedmachine-learning module 812 may be implemented as any hardware and/orsoftware module. Supervised machine-learning module 812 generates usinga supervised machine-learning process a menu model 172 that receives thefood tolerance instruction set 148 as an input and produces an outputcontaining a plurality of menu options 176 utilizing the menu trainingset 164. Menu model 172 includes any machine-learning model as describedabove. Menu model 172 may include any supervised machine-learning modelas described above. Menu model 172 includes any machine learning processthat is a linear or polynomial regression algorithm, the menu model mayinclude an equation; it may be a set of instructions to generate outputsbased on inputs which is derived using the machine-learning algorithm,and the like. Supervised machine-learning process include any of thesupervised machine-learning processes as described above in reference toFIG. 1. Menu generator module 160 may select menu model 172 from menudatabase 168. Menu model 172 may be precalculated and stored within menudatabase 168. A plurality of menu options 176 include a list ofavailable dish choices that may be selected for a particular meal asdescribed above in more detail in reference to FIG. 1. Menu options 176may include options that a user may cook at home or options that a usermay order from particular food preparers as described above in moredetail in reference to FIG. 1. Menu generator module 160 displays aplurality of menu options 176 on a graphical user interface 152 locatedon a graphical user interface 152 located on a processor 104.

Referring now to FIG. 9, an exemplary embodiment 900 of menu database168 is illustrated. Menu database 168 may be implemented as any datastructure suitable for use as clustering database 120 as described abovein reference to FIG. 1. Menu database 168 may store menu training set164, menu model 172, training set models, and/or model training sets.One or more tables contained within menu database 168 may includemushroom avoidance table 904; mushroom avoidance table 904 may includemenu training set 164 that include mushroom food items containing a foodtolerance score of avoidance. One or more tables contained within menudatabase 168 may include coffee minimize table 908; coffee minimizetable 908 may include menu training set 164 that include coffee fooditems containing a food tolerance score of minimize. One or more tablescontained within menu database 168 may include artichoke enjoyment table912; artichoke enjoyment table 912 may include menu training set 164that include artichoke food items containing a food tolerance score ofenjoyment. One or more tables contained within menu database 168 mayinclude buckwheat superfood table 916; buckwheat superfood table 916 mayinclude menu training set 164 that include buckwheat food itemscontaining a food tolerance score of superfood. One or more tablescontained within menu database 168 may include training set model table920; training set model table 920 may include one or more model trainingsets and/or training set models. One or more tables contained withinmenu database 168 may include menu model 172 table 924; menu model 172table 924 may include one or more menu model 172.

Referring now to FIG. 10, an exemplary embodiment 1000 of local selectormodule 180 is illustrated. Local selector module 180 may be implementedas any hardware and/or software module. Local selector module 180 isconfigured receive a plurality of meal option inputs 184 from a mealpreparer device 188 wherein the meal option inputs 184 contain availablemenu listings. Local selector module is configured to receive an elementof user geolocation data and receive a plurality of meal option inputsfrom a meal preparer device 188 related to the element of usergeolocation data. An “element of user geolocation data” as used in thisdisclosure, includes any data describing where a particular user islocated at any given moment. An element of user geolocation data may beobtained from a user device, from a global positioning system (GPS)input, cell-tower triangulation data and the like. Local selector modulemay receive a plurality of meal option inputs from a meal preparerdevice 188 related to an element of user geolocation data. Meal optioninputs may be related to an element of user geolocation data when mealoption inputs are received from meal preparer device 188 who may providemeals in a location where user is currently located as indicated byuser's element of geolocation data. For instance and without limitation,an element of user geolocation data may indicate that a user iscurrently located in Houston, Tex. In such an instance, local selectormodule may receive a plurality of meal option inputs from meal preparerdevice 188 located within Houston, Tex. or from meal preparer device 188that provide meals to users located within Houston, Tex. Meal optioninputs 184 may be received utilizing any network topography as describedherein. Meal option inputs 184 include data describing available mealsthat a user can order and purchase that are cooked and prepared frommeal preparers. Meal preparers include any of the meal preparers asdescribed above in reference to FIG. 1. Local selector module 180receives a plurality of menu options 176 from menu generator module 160utilizing any network methodology as described herein.

With continued reference to FIG. 10, local selector module 180 mayinclude a k-nearest neighbors module 1004. K-nearest neighbors (KNN)module 1004 may be implemented as any hardware and/or software module.KNN module 1004 is configured to generate a k-nearest neighborsalgorithm 192 utilizing the plurality of meal option inputs 184 and theplurality of menu options 176. K-nearest neighbors algorithm 192includes a lazy-learning process as described above in more detail inreference to FIG. 1. KNN module may modify meal options inputs and aplurality of menu options 176 by representing meal option inputs 184 anda plurality of menu options 176 as vectors. Vectors may includemathematical representations of meal option inputs 184 and a pluralityof menu options 176. Vectors may include n-tuple of values which mayrepresent a measurement or other quantitative value associated with agiven category of data, or attribute. Vectors may be represented inn-dimensional space using an axis per category of value represented inn-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. In an embodiment, KNN module 1004 may calculatean initial heuristic ranking association between inputs and elements ofmeal option inputs 184 and a plurality of menu options 176. Initialheuristic may include selecting some number of highest-rankingassociations and/or training data elements. KNN module 1004 may performone or more processes to modify and/or format meal option inputs 184 anda plurality of menu options 176. Meal option inputs 184 and a pluralityof menu options 176 may contain “N” unique features, whereby a datasetcontained within meal option inputs 184 and a plurality of menu options176 may be represented as a vector may contain a vector of length “N”whereby entry “I” of the vector represents that data point's value forfeature “I.” Each vector may be mathematically represented as a point in“R{circumflex over ( )}N.” For instance and without limitation, KNNmodule 1004 may modify entries contained within meal option inputs 184and a plurality of menu options 176 to contain consistent forms of avariance. KNN module 1004 performs K-nearest neighbor algorithm byclassifying datasets contained within meal option inputs 184 and aplurality of menu options 176. Meal option inputs 184 and a plurality ofmenu options 176 may be represented as an “M×N” matrix where “M” is thenumber of data points contained within meal option inputs 184 and aplurality of menu options 176 and “N” is the number of featurescontained within meal option inputs 184 and a plurality of menu options176. Classifying datasets contained within meal option inputs 184 and aplurality of menu options 176 may include computing a distance valuebetween an item to be classified such as a meal option and each datasetcontained within meal option inputs 184 and a plurality of menu options176 which may be represented as a vector. A value of “k” may bepre-determined or selected that will be used for classifications. In anembodiment, value of “k” may be selected as an odd number to avoid atied outcome. In an embodiment, value of “k” may be decided by KNNmodule 1004 arbitrarily or value may be cross validated to find anoptimal value of “k.”. KNN module 1004 may then select a distance metricthat will be used in K-nearest neighbor algorithm. In an embodiment, KNNmodule 1004 may utilize Euclidean distance which may be measure distanceby subtracting the distance between a training data point and thedatapoint to be classified such as a particular meal option. In anembodiment, Euclidean distance may be calculated by a formularepresented as:

${E\left( {x,y} \right)} = {\sqrt{\sum\limits_{i = 0}^{n}\left( {{xi} - {yi}} \right)^{2}}.}$In an embodiment, KNN module 1004 may utilize metric distance of cosinesimilarity which may calculate distance as the difference in directionbetween two vectors which may be represented as: similarity=cos0=A×B÷∥A∥∥B∥. After selection of “k” value, and selection of distancemeasurement by KNN module 1004, KNN module 1004 may partition in“R{circumflex over ( )}N” into sections. Sections may be calculatedusing the distance metric and the available data points contained withinmeal option inputs 184 and a plurality of menu options 176. KNN module1004 may calculate a plurality of optimal vector output; in such aninstance, where a plurality of matching entries are returned, optimalvector output may be obtained by aggregating matching entries includingany suitable method for aggregation, including component-wise additionfollowed by normalization component-wise calculation of arithmeticmeans, or the like. KNN module 1004 identifies a plurality of compatiblemeal options 196 as a function of generating a k-nearest neighboralgorithm.

With continued reference to FIG. 10, local selector module 180 mayinclude k-means clustering module 1008. K-means clustering module may beimplemented as any hardware and/or software module. K-means clusteringmodule is configured to generate a k-means clustering algorithmutilizing the meal option inputs 184 and the plurality of menu options176. K-means clustering module 1008 receives clustering dataset as inputand outputs a definite number of classified data entry clusters thateach contain cluster data entries. K-means clustering algorithm module1008 may determine k-value that will set a fixed number of classifieddata entry clusters as outputs utilizing any of the methods as describedabove in reference to FIG. 1. In an embodiment, k-value may be selectedbased generating k-means clustering algorithm repeatedly until a k-valueis averaged and selected. In yet another non-limiting example, a k-valuemay be selected based on a particular clustering dataset that may bebest suited for a particular k-value. K-means clustering module 1008receives as input unclassified clustering dataset. Unclassifiedclustering dataset may include any of the unclassified clusteringdataset as described above in more detail in reference to FIG. 1.K-means clustering algorithm module 1008 outputs classified data entryclusters. Data entry clusters may be classified by k-means clusteringalgorithm module 1008 using predictive modeling that approximates amapping function from input variables to discrete output variables.Classification may be performed utilizing classification algorithms thatinclude for example decision trees, naïve bayes, artificial neuralnetworks, boosting, kernel methods, and/or k-nearest neighbors algorithm192. K-means clustering module 1008 may generate a soft k-meansclustering algorithm wherein a “soft k-means clustering algorithm” asused in this disclosure includes a k-means clustering algorithm where acluster data entry may be selected and/or assigned to multiple clustersof the definite number of classified data entry cluster. For instanceand without limitation, k-means clustering algorithm module may generatea soft k-means clustering algorithm that has a k-value of seven andwhere a particular cluster data entry may be selected and assigned tothree of the seven classified data entry cluster. K-means clusteringalgorithm module may generate a hard k-means clustering algorithmwherein a “hard k-means clustering algorithm” as used in this disclosureincludes a k-means clustering algorithm where a cluster data entry maybe selected to be assigned to one cluster of the definite number ofclassified data entry cluster. For instance and without limitation,k-means clustering module may generate a hard k-means clusteringalgorithm that has a k-value of seven and where a particular clusterdata entry may be selected and assigned to one of the seven classifieddata entry cluster. K-means clustering algorithm 136 module may select ahard k-means algorithm and/or a soft k-means algorithm based on expertinput as described in more detail below. In an embodiment, k-meansclustering algorithm module may select a hard k-means algorithm and/or asoft k-means algorithm based on learned associations between clusteringdataset and classified data entry outputs such as by learnedassociations such as from a clustering learner.

With continued reference to FIG. 10, local selector module may includeloss function module. Loss function module may be implemented as anyhardware and/or software module. Loss function module 1012 is configuredto receive a user input from a user client device wherein a user inputincludes a meal option element indicator, generate a loss functionutilizing the user input and the plurality of meal option inputs,minimize the loss function, and select a compatible meal option from theplurality of compatible meal options as a function of minimizing theloss function. Meal option element indicator includes any of the mealoption element indicators as described above in reference to FIG. 1. Forinstance and without limitation, meal option element may include anindicator describing how much money a user is willing to pay for aparticular compatible meal or how much money a user has budgeted for aparticular month's worth of compatible meals. Meal option element mayinclude a particular cooking preference such as an indication that auser prefers a meal preparer to prepare a compatible meal usingMediterranean Sea salt as opposed to table salt. Meal option element mayinclude an indicator about particular cooking methods a user preferssuch as a user who prefers a steak to be cooked medium rare or a soup tobe prepared in a raw form. Loss function module generates a lossfunction utilizing any of the methods as described above in reference toFIG. 1. Loss function module minimizes the loss function and selects acompatible meal option as a function of minimizing the loss function.

Referring now to FIG. 11, an exemplary embodiment of a method 1100 ofidentifying compatible meal options is illustrated. At step 1105 aprocessor receives a user biological marker wherein the user biologicalmarker contains a plurality of user body measurements. A user biologicalmarker may include any of the user biological markers as described abovein reference to FIGS. 1-10. User body measurements may include any ofthe user body measurements as described above in reference to FIGS.1-10. In an embodiment, a plurality of user body measurements mayinclude at least a microbiome body measurement, at least a gut-wall bodymeasurement and at least a genetic body measurement as described abovein more detail in reference to FIG. 1. A microbiome body measurementincludes any of the microbiome body measurements as described above inmore detail in reference to FIG. 1. A gut-wall body measurement includesany of the gut-wall body measurements as described above in more detailin reference to FIG. 1. A genetic body measurement includes any of thegenetic body measurements as described above in more detail in referenceto FIG. 1.

With continued reference to FIG. 11, at step 1110 a processor selects aclustering dataset from a clustering database wherein the clusteringdataset further comprises a plurality of unclassified datapoints.Selecting a clustering dataset may be performed utilizing any of themethods as described above in reference to FIGS. 1-10. Selecting aclustering dataset includes classifying a biological marker to a bodydimension, generating a classification label containing a body dimensionlabel, and selecting a clustering dataset as a function of matching thebody dimension label to a clustering dataset containing unclassifieddatapoints related to the body dimension label. This may be performedutilizing any of the methods as described above in reference to FIGS.1-10. Body dimension may include any of the body dimensions describedabove in reference to FIG. 1.

With continued reference to FIG. 11, at step 1115 a processor generatesa hierarchical clustering algorithm using the clustering dataset asinput and wherein the hierarchical clustering algorithm outputs adefinite number of classified dataset clusters each containing a clusterlabel. Generating a hierarchical clustering algorithm may be performedutilizing any of the methods as described above in reference to FIGS.1-11.

With continued reference to FIG. 11, at step 1120 a processor assignsthe plurality of user body measurements to a first classified datasetcluster containing a cluster label. Assigning a plurality of user bodymeasurements to a first classified dataset cluster containing a clusterlabel may be performed utilizing any of the methods as described abovein reference to FIGS. 1-11.

With continued reference to FIG. 11, at step 1125 a processor selectsthe first classified dataset cluster containing the cluster label.Selecting a first classified dataset cluster may include generatingcluster labels that contain body dimensions. In an embodiment, aprocessor may receive a plurality of user body measurements, generate aclustering algorithm using the user body measurements as input andwherein the clustering algorithm outputs a plurality of cluster labelscontaining a body dimension, and select a first classified datasetcluster as a function of a body dimension. In an embodiment, experts mayprovide input as to particular body dimension labels and hierarchy ofselecting a particular first classified dataset cluster containing aparticular body dimension label.

With continued reference to FIG. 11, at step 1130 a processor selects afood training set from a food database as a function of the userbiological marker wherein the food training set correlates user bodymeasurements to food tolerance scores. Processor may select a foodtraining set utilizing any of the methods as described above inreference to FIGS. 1-10.

With continued reference to FIG. 11, at step 1135 a processor generatesusing a supervised machine-learning process a food model that receivesthe first assigned user body data element as an input and produces anoutput containing a food tolerance score. Supervised machine-learningprocess may include any of the supervised machine-learning processes asdescribed above in reference to FIG. 1. Food model may include any ofthe food models as described above in reference to FIGS. 1-10. In anembodiment, a food model may be pre-calculated and loaded into fooddatabase and selected by a processor from food database.

With continued reference to FIG. 11, at step 1140 a processor generatesa food tolerance instruction set using the food tolerance score. Foodtolerance instruction set may include any of the food toleranceinstruction sets as described above in reference to FIGS. 1-10.Generating a food tolerance instruction set may be done utilizing any ofthe methods as described above in reference to FIGS. 1-10. Foodtolerance instruction set may be generated by receiving a user entryfrom a user client device containing a food tolerance aversion input andfiltering a food tolerance instruction set as a function of a userinput. Food tolerance aversion may include any of the food toleranceaversions as described above in reference to FIGS. 1-10.

With continued reference to FIG. 11, at step 1145 a processor displayson a graphical user interface the output containing the food toleranceinstruction set containing the food tolerance score. Food toleranceinstruction set may be displayed by particular categories oftolerability and/or food items as described above in more detail inreference to FIGS. 1-10.

With continued reference to FIG. 11, at step 1150 a processor selects amenu training set from a menu database as a function of the foodtolerance instruction set wherein the menu training set correlates foodtolerance scores to menu options. Menu training data includes any of themenu training data as described above in reference to FIGS. 1-10. Menutraining data may be selected by receiving a model training setcorrelating food tolerance instruction sets to menu training sets andgenerating using a supervised machine-learning process and the modeltraining set a training set model that receives a food toleranceinstruction set as input and produces an output containing menu trainingset. This may be performed utilizing any of the methods as describedabove in reference to FIGS. 1-10. Menu training set may also be selectedby executing a lazy learning process as a function of model training setand food tolerance instruction set and producing an output containingmenu training set. This may be performed utilizing any of the methods asdescribed above in reference to FIGS. 1-10.

With continued reference to FIG. 11, at step 1155 a processor generatesusing a supervised machine-learning process a menu model that receivesthe food tolerance instruction set as an input and produces an outputcontaining a plurality of menu options utilizing the menu training set.Supervised machine-learning process may include any of the supervisedmachine-learning processes as described above in reference to FIGS.1-10. Menu model may include any of the menu models as described abovein reference to FIGS. 1-10. In an embodiment, menu models may beprecalculated and preloaded into menu database. A processor may select amenu model from menu database.

With continued reference to FIG. 11, at step 1160 a processor displayson the graphical user interface the output containing the plurality ofmenu options. This may be performed utilizing any of the methods asdescribed above in reference to FIGS. 1-11.

With continued reference to FIG. 11, at step 1165 a processor receives aplurality of meal option inputs from a meal preparer device 188 whereinthe meal option inputs contain available menu listings. A processor mayreceive a plurality of meal option inputs utilizing any networkmethodology as described herein. Meal option inputs include any of themeal-option inputs as described above in reference to FIGS. 1-10.

With continued reference to FIG. 11, at step 1170 a processor generatesa k-nearest neighbors algorithm utilizing the plurality of meal optioninputs and the plurality of menu options. K-nearest neighbors algorithmincludes any of the k-nearest neighbors algorithm as described above inreference to FIGS. 1-10. K-nearest neighbors algorithm may be generatedutilizing any of the methods as described above in reference to FIGS.1-10. A processor may also be configured to identify a plurality ofcompatible meal options by generating a k-means clustering algorithmutilizing a plurality of menu options and meal option inputs andidentify a compatible meal option as a function of generating thek-means clustering algorithm. K-means clustering algorithm includes anyof the k-means clustering algorithms as described above in reference toFIGS. 1-10.

With continued reference to FIG. 11, at step 1175 a processor identifiesa plurality of compatible meal options as a function of generating thek-nearest neighbor algorithm. In an embodiment, a processor may beconfigured to select a compatible meal option from a plurality ofcompatible meal options. A processor is configured to receive a userinput from a user client device where the user input includes a mealoption element indicator. Meal option element indicator includes any ofthe meal option element indicators as described above. For instance andwithout limitation, meal option element indicator may include a userentry regarding how much money a user is willing to spend for aparticular meal or how soon a user would like a meal prepared anddelivered. A processor generates a loss function utilizing a user inputand a plurality of meal option inputs. A processor minimizes the lossfunction and selects a compatible meal option from the plurality ofcompatible meal options as a function of minimizing the loss function.

With continued reference to FIG. 11, at step 1180 a processor displaysthe plurality of compatible meal options on the graphical userinterface. This may be performed utilizing any of the methods asdescribed above in reference to FIGS. 1-10.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 12 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1200 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1200 includes a processor 104 1204 and amemory 1208 that communicate with each other, and with other components,via a bus 1212. Bus 1212 may include any of several types of busstructures including, but not limited to, a memory bus, a memorycontroller, a peripheral bus, a local bus, and any combinations thereof,using any of a variety of bus architectures.

Memory 1208 may include various components (e.g., machine-readablemedia) including, but not limited to, a random access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1216 (BIOS), including basic routines thathelp to transfer information between elements within computer system1200, such as during start-up, may be stored in memory 1208. Memory 1208may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1220 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1208 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1200 may also include a storage device 1224. Examples ofa storage device (e.g., storage device 1224) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1224 may beconnected to bus 1212 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1224 (or one or more components thereof) may be removably interfacedwith computer system 1200 (e.g., via an external port connector (notshown)). Particularly, storage device 1224 and an associatedmachine-readable medium 1228 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1200. In one example,software 1220 may reside, completely or partially, withinmachine-readable medium 1228. In another example, software 1220 mayreside, completely or partially, within processor 104 1204.

Computer system 1200 may also include an input device 1232. In oneexample, a user of computer system 1200 may enter commands and/or otherinformation into computer system 1200 via input device 1232. Examples ofan input device 1232 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1232may be interfaced to bus 1212 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1212, and any combinations thereof. Input device 1232may include a touch screen interface that may be a part of or separatefrom display 1236, discussed further below. Input device 1232 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1200 via storage device 1224 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1240. A networkinterface device, such as network interface device 1240, may be utilizedfor connecting computer system 1200 to one or more of a variety ofnetworks, such as network 1244, and one or more remote devices 1248connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1244, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1220, etc.) may be communicated to and/or fromcomputer system 1200 via network interface device 1240.

Computer system 1200 may further include a video display adapter 1252for communicating a displayable image to a display device, such asdisplay device 1236. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1252 and display device 1236 maybe utilized in combination with processor 104 1204 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1200 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1212 via a peripheral interface 1256.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for identifying compatible meal optionsthe system comprising a processor wherein the processor furthercomprises: a body analysis module wherein the body analysis module isfurther configured to: receive a user biological marker, wherein theuser biological marker contains a plurality of user body measurementsincluding an element of microbiome data, an element of physical usergut-wall data, and an element of user genetic data; select a clusteringdataset from a clustering database wherein the clustering datasetfurther comprises a plurality of unclassified datapoints; generate ahierarchical clustering algorithm using the clustering dataset as input,wherein the hierarchical clustering algorithm outputs a definite numberof classified dataset clusters each containing a cluster label; assignthe plurality of user body measurements to a first classified datasetcluster containing a first cluster label; and select the firstclassified dataset cluster containing the first cluster label; a foodanalysis module wherein the food analysis module is further configuredto: receive from the body analysis module the first classified datasetcluster containing the first cluster label and the user biologicalmarker; select a food training set from a food database as a function ofthe user biological marker wherein the food training set correlates theelement of microbiome data, the element of physical user gut-wall data,and the element of user genetic data to numerical food tolerance scores;generate using a supervised machine-learning process a food model thatreceives the assigned plurality of user body measurements as an inputand produces an output containing a numerical food tolerance scoreutilizing the food training set; generate a food tolerance instructionset using the numerical food tolerance score; and display on a graphicaluser interface located on the processor the output containing the foodtolerance instruction set; a menu generator module wherein the menugenerator module is further configured to: receive the food toleranceinstruction set from the food analysis module; select a menu trainingset from a menu database as a function of the food tolerance instructionset wherein the menu training set correlates numerical food tolerancescores to menu options; generate using a supervised machine-learningprocess a menu model that receives the food tolerance instruction set asan input and produces an output containing a plurality of menu optionsutilizing the menu training set; and display on the graphical userinterface located on the processor the output containing the pluralityof menu options; and a local selector module wherein the local selectormodule is further configured to: receive a plurality of meal optioninputs from at least a meal preparer device wherein the meal optioninputs contain available menu listings; receive the output containingthe plurality of menu options from the menu generator module; generate ak-nearest neighbors algorithm utilizing the plurality of meal optioninputs and the plurality of menu options; identify a plurality ofcompatible meal options as a function of generating the k-nearestneighbor algorithm; and display the plurality of compatible meal optionson the graphical user interface located on the processor.
 2. The systemof claim 1, wherein the plurality of user body measurements furthercomprises at least a microbiome body measurement and at least a geneticbody measurement.
 3. The system of claim 1, wherein selecting aclustering dataset further comprises: classifying a biological marker toa body dimension; generating a classification label containing a bodydimension label; and selecting a clustering dataset as a function ofmatching the body dimension label to a clustering dataset containingunclassified datapoints related to the body dimension label.
 4. Thesystem of claim 1, wherein selecting a first classified dataset clustercontaining a first cluster label further comprises: receiving theplurality of user body measurements; generating a clustering algorithmusing the user body measurements as input and wherein the clusteringalgorithm outputs a plurality of cluster labels containing a bodydimension; and selecting the first classified dataset cluster as afunction of the body dimension.
 5. The system of claim 1, whereingenerating the food tolerance instruction set further comprises:receiving a user entry from a user client device containing a user foodtolerance aversion input; and filtering the food tolerance instructionset as a function of the user entry.
 6. The system of claim 1, whereinselecting a menu training set further comprises: receiving a modeltraining set correlating food tolerance instruction sets to menutraining sets; and generating using a supervised machine-learningprocess and the model training set a training set model that receivesthe food tolerance instruction set as an input and produces an outputcontaining menu training set.
 7. The system of claim 6 furthercomprising executing a lazy learning process as a function of the modeltraining set and the food tolerance instruction set and producing anoutput containing menu training set.
 8. The system of claim 1, whereinthe local selector module is further configured to: receive a user inputfrom a user client device wherein the user input further comprises ameal option element indicator; generate a loss function utilizing theuser input and the plurality of meal option inputs; minimize the lossfunction; and select a compatible meal option from the plurality ofcompatible meal options as a function of minimizing the loss function.9. The system of claim 1, wherein the local selector module is furtherconfigured to: receive an element of user geolocation data; and receivea plurality of meal option inputs from at least a meal preparer devicerelated to the element of user geolocation data.
 10. The system of claim1, wherein the local selector module is further configured to: generatea k-means clustering algorithm utilizing the plurality of menu optionsand the plurality of meal option inputs; and identify a compatible mealoption as a function of generating the k-means clustering algorithm. 11.A method of identifying compatible meal options the method comprising:receiving by a processor a user biological marker wherein the userbiological marker contains a plurality of user body measurementsincluding an element of microbiome data, an element of physical usergut-wall data, and an element of user genetic data; selecting by theprocessor a clustering dataset from a clustering database wherein theclustering dataset further comprises a plurality of unclassifieddatapoints; generating by the processor a hierarchical clusteringalgorithm using the clustering dataset as input and wherein thehierarchical clustering algorithm outputs a definite number ofclassified dataset clusters each containing a cluster label; assigningby the processor the plurality of user body measurements to a firstclassified dataset cluster containing a first cluster label; selectingby the processor the first classified dataset cluster containing thefirst cluster label; selecting by the processor a food training set froma food database as a function of the user biological marker wherein thefood training set correlates the element of microbiome data, the elementof physical user gut-wall data, and the element of user genetic data tonumerical food tolerance scores; generating by the processor using asupervised machine-learning process a food model that receives theassigned plurality of user body measurements as an input and produces anoutput containing a numerical food tolerance score utilizing the foodtraining set; generating by the processor a food tolerance instructionset using the numerical food tolerance score; displaying by theprocessor on a graphical user interface the output containing the foodtolerance instruction set containing the numerical food tolerance score;selecting by the processor a menu training set from a menu database as afunction of the food tolerance instruction set wherein the menu trainingset correlates numerical food tolerance scores to menu options;generating by the processor using a supervised machine-learning processa menu model that receives the food tolerance instruction set as aninput and produces an output containing a plurality of menu optionsutilizing the menu training set; displaying by the processor on thegraphical user interface the output containing the plurality of menuoptions; receiving by the processor a plurality of meal option inputsfrom at least a meal preparer device wherein the meal option inputscontain available menu listings; generating by the processor a k-nearestneighbors algorithm utilizing the plurality of meal option inputs andthe plurality of menu options; identifying by the processor a pluralityof compatible meal options as a function of generating the k-nearestneighbor algorithm; and displaying by the processor the plurality ofcompatible meal options on the graphical user interface.
 12. The methodof claim 11, wherein the plurality of user body measurements furthercomprises at least a microbiome body measurement and at least a geneticbody measurement.
 13. The method of claim 11, wherein selecting aclustering dataset further comprises: classifying a biological marker toa body dimension; generating a classification label containing a bodydimension label; and selecting a clustering dataset as a function ofmatching the body dimension label to a clustering dataset containingunclassified datapoints related to the body dimension label.
 14. Themethod of claim 11, wherein selecting a first classified dataset clustercontaining the cluster label further comprises: receiving the pluralityof user body measurements; generating a clustering algorithm using theplurality of user body measurements as input and wherein the clusteringalgorithm outputs a plurality of cluster labels containing a bodydimension; and selecting a first classified dataset cluster as afunction of the body dimension.
 15. The method of claim 11, whereingenerating the food tolerance instruction set further comprises:receiving a user entry from a user client device containing a user foodtolerance aversion input; and filtering the food tolerance instructionset as a function of the user entry.
 16. The method of claim 11, whereinselecting a menu training set further comprises: receiving a modeltraining set correlating food tolerance instruction sets to menutraining sets; and generating using a supervised machine-learningprocess and the model training set a training set model that receivesthe food tolerance instruction set as an input and produces an outputcontaining menu training set.
 17. The method of claim 16 furthercomprising executing a lazy learning process as a function of the modeltraining set and the food tolerance instruction set and producing anoutput containing menu training set.
 18. The method of claim 11, furthercomprising: receiving, by a processor, a user input from a user clientdevice wherein the user input further comprises a meal option elementindicator; generating, by a processor, a loss function utilizing theuser input and the plurality of meal option inputs; minimizing, by theprocessor, the loss function; and selecting, by a processor, acompatible meal option from the plurality of compatible meal options asa function of minimizing the loss function.
 19. The method of claim 11,further comprising: receiving, by the processor, an element of usergeolocation data; and receiving, by the processor, a plurality of mealoption inputs from at least a meal preparer device related to theelement of user geolocation data.
 20. The method of claim 11, furthercomprising: generating, by the processor, a k-means clustering algorithmutilizing the plurality of menu options and the meal option inputs; andidentifying, by the processor, a compatible meal option as a function ofgenerating the k-means clustering algorithm.